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Network Analysis in Finance and Economics

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Duration

28/03/2022 - 13/04/2022

Early Bird

-10% before 15 February

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Level

Intermediate

Approach

Quantitative

Delivery mode

Online

Course closed

Over the last years, network analysis has become an active topic of research, with numerous applications in macroeconomics and finance. In a nutshell, network analysis is concerned with representing the interconnections of a large panel as a graph: the vertices of the graph represent the variables in the panel, and the presence of an edge between two vertices denotes the presence of some appropriate measure of dependence between the two variables. Dependence can derive from direct exposures or from indirect or common exposures. From an economic perspective, the interest on networks has been boosted by the research of, inter alia, Acemoglu et al. (2012), which shows that individual entities can have a non-negligible effect on the aggregate behaviour of the economy when the system has a high degree of interconnectedness. Especially since the 2008 global financial crisis, the interest in analysing the role of network structure in transmitting – or dissipating – stress has grown significantly.

In this course, participants will be introduced to the theoretical framework and literature behind network analysis techniques for applications in finance and economics. Practical application of the methodologies will be presented in the form of examples, case studies and exercises. These sessions will be supported by code written in Python, with some parts available also in R.

I found this course extremely useful and well done! I learnt a lot and I am looking forward to put into practice the knowledge I acquired.

Participant in the 2021 edition of the course

Christian Brownlees

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Iman van Lelyveld

Part-time Professor

Florence School of Banking and Finance

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Natalie Kessler

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Jan Trevisan

Online Training Officer

Robert Schuman Centre for Advanced Studies

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Master’s Degree in Economics
Basic knowledge of coding in Python

The course will not require active coding, but participants should be able to understand and execute existing code.

The course is structured in two parts:

In the first part, from the 28 March to 5 April, you will follow a series of video lectures in the following modules:

Networks Basic Concepts
Direct exposure networks
Direct contagion: the classics
Capturing network structure
Indirect exposure networks
Crowded trading
What are the policy implications
Network Models for Large Panels

During the second part, from 6 April to 13 April, you will apply the theory into practice by participating in a series of live lectures and exercises, as well as individual assignments.

Calendar of the live classes

Monday 28 March, 1:00 – 2:45 PM – Kickoff live session

Wednesday 6 April, 12:00 noon – 2:00 PM – First live session

Monday 11 April, 12:00 noon – 2:00 PM – Second live session

Wednesday 13 April, 1:00 – 2:00 PM – Closing live session

The Online course takes place from 28 March to 13 April 2022.

Early bird fee applies until 15 February 2022

Early bird € 945 (Standard fee: € 1050) – Private Sector
Early bird €855 (Standard fee: € 950) – Public Authorities (e.g. National Competent Authorities, Central Banks) and European Institutions –
€ 750 (No early bird) – Full-Time Professors, PhD Students, Research Associates
Please submit a certificate attesting your status of Professor, PhD Student or Research Associate to fbf@eui.eu before registering. FBF secretariat will provide you with a code to register. *Seats for academics are limited.

Please note that the payment must be settled one week before the start of the course.

Limited seats per institution

A certificate of attendance will be provided to all participants after the course.

CANCELLATION POLICY:

In case you can no longer attend the course, you are required to inform the organisers by sending an email to fbf@eui.eu in order to free a seat for participants in the waiting list.
In case of frequent cancellations, FBF reserves the right not to accept further registrations from the same person.